Description Usage Arguments Value Author(s) References Examples
Computes the multitrait genotypeideotype distance index, MGIDI, (Olivoto and Nardino, 2020), used to select genotypes in plant breeding programs based on multiple traits.The MGIDI index is computed as follows: \loadmathjax \mjsdeqnMGIDI_i = \sqrt\sum\limits_j = 1^f(F_ij  F_j)^2
where \mjseqnMGIDI_i is the multitrait genotypeideotype distance index for the ith genotype; \mjseqnF_ij is the score of the ith genotype in the jth factor (i = 1, 2, ..., g; j = 1, 2, ..., f), being g and f the number of genotypes and factors, respectively, and \mjseqnF_j is the jth score of the ideotype. The genotype with the lowest MGIDI is then closer to the ideotype and therefore should presents desired values for all the analyzed traits.
1 2 3 4 5 6 7 8 9 10 
.data 
An object fitted with the function 
use_data 
Define which data to use if 
SI 
An integer (0100). The selection intensity in percentage of the total number of genotypes. 
mineval 
The minimum value so that an eigenvector is retained in the factor analysis. 
ideotype 
A vector of length 
weights 
Optional weights to assign for each trait in the selection
process. It must be a numeric vector of length equal to the number of
traits in 
use 
The method for computing covariances in the presence of missing
values. Defaults to 
verbose 
If 
An object of class mgidi
with the following items:
data The data used to compute the factor analysis.
cormat The correlation matrix among the environments.
PCA The eigenvalues and explained variance.
FA The factor analysis.
KMO The result for the KaiserMeyerOlkin test.
MSA The measure of sampling adequacy for individual variable.
communalities The communalities.
communalities_mean The communalities' mean.
initial_loadings The initial loadings.
finish_loadings The final loadings after varimax rotation.
canonical_loadings The canonical loadings.
scores_gen The scores for genotypes in all retained factors.
scores_ide The scores for the ideotype in all retained factors.
gen_ide The distance between the scores of each genotype with the ideotype.
MGIDI The multitrait genotypeideotype distance index.
contri_fac The relative contribution of each factor on the MGIDI value. The lower the contribution of a factor, the close of the ideotype the variables in such factor are.
contri_fac_rank, contri_fac_rank_sel The rank for the contribution of each factor for all genotypes and selected genotypes, respectively.
sel_dif The selection differential for the variables.
stat_gain A descriptive statistic for the selection gains. The minimum, mean, confidence interval, standard deviation, maximum, and sum of selection gain values are computed. If traits have negative and positive desired gains, the statistics are computed for by strata.
sel_gen The selected genotypes.
Tiago Olivoto tiagoolivoto@gmail.com
Olivoto, T., and Nardino, M. (2020). MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics. doi: 10.1093/bioinformatics/btaa981
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  library(metan)
# simulate a data set
# 10 genotypes
# 5 replications
# 4 traits
df <
g_simula(ngen = 10,
nrep = 5,
nvars = 4,
gen_eff = 35,
seed = c(1, 2, 3, 4))
# run a mixedeffect model (genotype as random effect)
mod <
gamem(df,
gen = GEN,
rep = REP,
resp = everything())
# BLUPs for genotypes
gmd(mod, "blupg")
# Compute the MGIDI index
# Default options (all traits with positive desired gains)
# Equal weights for all traits
mgidi < mgidi(mod)
gmd(mgidi, "MGIDI")
# Higher weight for traits V1 and V4
# This will increase the probability of selecting H7 and H9
# 30% selection pressure
mgidi2 <
mgidi(mod,
weights = c(1, .2, .2, 1),
SI = 30)
gmd(mgidi2, "MGIDI")
# plot the contribution of each factor on the MGIDI index
p1 < plot(mgidi, type = "contribution")
p2 < plot(mgidi2, type = "contribution")
p1 + p2
# Positive desired gains for V1, V2 and V3
# Negative desired gains for V4
mgidi3 <
mgidi(mod,
ideotype = c("h, h, h, l"))

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